There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21590

Cancer statistics, 2020.

As a fascinating conjugated polymer, graphitic carbon nitride (g-C3N4) has become a new research hotspot and drawn broad interdisciplinary attention as a metal-free and visible-light-responsive photocatalyst in the arena of solar energy conversion and environmental remediation. This is due to its appealing electronic band structure, high physicochemical stability, and “earth-abundant” nature. This critical review summarizes a panorama of the latest progress related to the design and construction of pristine g-C3N4 and g-C3N4-based nanocomposites, including (1) nanoarchitecture design of bare g-C3N4, such as hard and soft templating approaches, supramolecular preorganization assembly, exfoliation, and template-free synthesis routes, (2) functionalization of g-C3N4 at an atomic level (elemental doping) and molecular level (copolymerization), and (3) modification of g-C3N4 with well-matched energy levels of another semiconductor or a metal as a cocatalyst to form heterojunction nanostructures. The constructi...

Graphitic Carbon Nitride (g-C3N4)-Based Photocatalysts for Artificial Photosynthesis and Environmental Remediation: Are We a Step Closer To Achieving Sustainability?

/pdf/identifying-and-anticipating-tipping-points-in-lake-2f9wv47231.pdf

Identifying and anticipating tipping points in lake ecosystems

Fabrication of fluorinated polyamide seawater reverse osmosis membrane with enhanced boron removal

Responses of bacterial communities and their carbon dynamics to subsoil exposure on the Loess Plateau.

With the wide use of smart phones, a large amount of GPS data are collected, while risks of privacy disclosure are also increasing. The de-anonymization attack is a typical attack which can infer the owner of an anonymous set of mobility traces. However, most existing works only consider spatial influences without considering temporal influences sufficiently. In this paper, we define a User Hidden Markov Model (UHMM) considering spatio-temporal influences, and exploit this model to launch the de-anonymization attack. Moreover, we conduct a set of experiments on a real-world dataset. The results show our approach is more accurate than other methods.

A De-anonymization Attack on Geo-Located Data Considering Spatio-temporal Influences

Cationic lytic peptides (CLPs) have shown promise in treating bacterial infection and cancer via selective disruption of bacterial or cancer cell membranes. In this work, we used a CLP, C6, as a nanocarrier for a hydrophobic anticancer agent, ellipticine (EPT). The size of the resulting C6–EPT complex was ∼190 nm. The in vitro studies using A549 lung cancer cells showed an enhanced anticancer activity of the C6–EPT complex compared to that of C6 or the EPT control. This enhancement was found to correlate with the membrane disruption induced by C6, which facilitated the entry of EPT into cells. More importantly, the C6–EPT complex showed a higher selectivity than that of C6 towards cancer cells upon comparison of their cytotoxicities against A549 cells and NIH-3T3 fibroblast cells. The enhanced therapeutic activity was also found in in vivo studies using an A549 tumor-bearing BALB/c nude mice model. This study provides a new CLP strategy for the development of multifunctional drug delivery systems.

Rong Wang

Papers

Identifying and anticipating tipping points in lake ecosystems

Fabrication of fluorinated polyamide seawater reverse osmosis membrane with enhanced boron removal

Responses of bacterial communities and their carbon dynamics to subsoil exposure on the Loess Plateau.

A De-anonymization Attack on Geo-Located Data Considering Spatio-temporal Influences

An amphipathic lytic peptide for enhanced and selective delivery of ellipticine